AZuRE, a Scalable System for Automated Term Disambiguation of Gene and Protein Names

Researchers, hindered by a lack of standard gene and protein-naming conventions, endure long, sometimes fruitless, literature searches. A system is described which is able to automatically assign gene names to their LocusLink ID (LLID) in previously unseen abstracts. The system is based on supervised learning and builds a model for each LLID. The training sets for all LLIDs are extracted automatically from MEDLINE references in the LocusLink and SwissProt databases. A validation was done of the performance for all 20,546 human genes with LLIDs. Of these, 7,344 produced good quality models (F-measure > 0.7, nearly 60% of which were > 0.9) and 13,202 did not, mainly due to insufficient numbers of known document references. A hand validation of MEDLINE documents for a set of 66 genes agreed well with the system's internal accuracy assessment. It is concluded that it is possible to achieve high quality gene disambiguation using scaleable automated techniques.

• Type: Conference
• Year: 2004
• Where: CSB
• Authors: Raf M. Podowski, John G. Cleary, Nicholas T. Goncharoff, Gregory Amoutzias, William S. Hayes
• Keywords: Bioinformatics, CSB 2004, Gene, Gene Names, Quality Gene Disambiguation